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    "# Soft Actor-Critic (SAC) with PyTorch\n",
    "\n",
    "In this notebook we will look at Soft Actor-Critic (SAC) algorithm using PyTorch. It builds on what we saw in `9.b-td3.ipynb` for TD3. However, it is not further improvement of TD3, rather something that came out in parallel of TD3.\n",
    "\n",
    "Click here to access the paper which proposed SAC originally: https://arxiv.org/pdf/1802.09477.pdf\n",
    "\n",
    "SAC forms a bridge between DDPG style deterministic policy gradients and stochastic policy optimization. \n",
    "\n",
    "##### SAC vs TD3\n",
    "\n",
    "Similar:\n",
    "1. Both use Mean Squared Bellman Error(MSBE) minimization towards a common target\n",
    "2. Common target is calculated using target Q networks which are obtained using polyack averaging\n",
    "3. Both use clipped double Q - minimum of two Q values to avoid overestimation\n",
    "\n",
    "Different:\n",
    "1. SAC uses entropy regularization which is absent in TD3\n",
    "2. In TD3 target-policy is used to calculate the next-state-actions while, in SAC we use **current policy** to get actions for next_states\n",
    "3. In TD3 target_policy uses smoothing by adding random noise to actions, However, in SAC the policy learnt is a stochastic one which provides a smoothing effect without any explicit noise addition.\n",
    "\n",
    "#### Q-Loss with Entropy-Regularization\n",
    "\n",
    "Entropy measures randomness in the distribution. Higher the entropy, higher (or flatter) is the distribution. A sharp peaked policy has all its probability centered around that peak and hence it will have low entropy. Entropy is given by:\n",
    "\n",
    "$$H(P) = \\underset{x \\sim P}{E}\\left[{-\\log P(x)}\\right] \\;\\;\\;\\;\\text{where P is distribution of random variable x}$$\n",
    "\n",
    "With entropy regularization, the policy is trained to maximize a trade-off between expected return and entropy with  $\\alpha$ controlling the trade-off. The updated maximization problem is:\n",
    "\n",
    "The policy is trained to maximize a trade-off between expected return and entropy, a measure of randomness in the policy.\n",
    "\n",
    "$$\\pi^* = \\arg \\max_{\\pi} \\underset{\\tau \\sim \\pi}{E}\\left[{ \\sum_{t=0}^{\\infty} \\gamma^t \\left( R(s_t, a_t, s_{t+1}) + \\alpha H(\\pi(\\cdot|s_t)) \\right)}\\right]$$\n",
    "\n",
    "In this setting, $V^{\\pi}$ is changed to include the entropy from every time step:\n",
    "\n",
    "$$V^{\\pi}(s) = \\underset{\\tau \\sim \\pi}{E} \\left[{ \\left. \\sum_{t=0}^{\\infty} \\gamma^t \\left( R(s_t, a_t, s_{t+1}) + \\alpha H\\left(\\pi(\\cdot|s_t)\\right) \\right) \\right| s_0 = s} \\right]$$\n",
    "\n",
    "and, $Q^{\\pi}$ is changed to include the entropy bonuses from every time step except the first:\n",
    "\n",
    "$$Q^{\\pi}(s,a) = \\underset{\\tau \\sim \\pi}{E}\\left[{ \\left. \\sum_{t=0}^{\\infty} \\gamma^t  R(s_t, a_t, s_{t+1}) + \\alpha \\sum_{t=1}^{\\infty} \\gamma^t H\\left(\\pi(\\cdot|s_t)\\right)\\right| s_0 = s, a_0 = a}\\right]$$\n",
    "\n",
    "With these definitions, $V^{\\pi}$ and $Q^{\\pi}$ are connected by:\n",
    "\n",
    "$$V^{\\pi}(s) = \\underset{a \\sim \\pi}{E}{Q^{\\pi}(s,a)} + \\alpha H\\left(\\pi(\\cdot|s)\\right)$$\n",
    "\n",
    "and the Bellman equation for $Q^{\\pi}$ is\n",
    "\n",
    "$$Q^{\\pi}(s,a) = \\underset{s' \\sim P \\\\ a' \\sim \\pi}E[\\left[{R(s,a,s') + \\gamma\\left(Q^{\\pi}(s',a') + \\alpha H\\left(\\pi(\\cdot|s')\\right) \\right)} \\right]\\\\\n",
    "= \\underset{s' \\sim P}E\\left[{R(s,a,s') + \\gamma V^{\\pi}(s')}\\right].$$\n",
    "\n",
    "The right hand side is an expectation which we will now convert into a sample estimate.\n",
    "\n",
    "\n",
    "$$Q^{\\pi}(s,a) \\approx r + \\gamma\\left(Q^{\\pi}(s',\\tilde{a}') - \\alpha \\log \\pi(\\tilde{a}'|s') \\right), \\;\\;\\;\\;\\;  \\tilde{a}' \\sim \\pi(\\cdot|s')$$\n",
    "\n",
    "where(s,a,r,s') come form replay buffer and $\\tilde{a}'$ comes from sampling the online/agent policy. In SAC, we do not use target network policy at all. \n",
    "\n",
    "\n",
    "Like TD3, SAC uses clipped Double Q and minimizes Mean Squared Bellman Error (MSBE). Putting it all together, the loss functions for the Q-networks in SAC are:\n",
    "\n",
    "$$L(\\phi_i, {\\mathcal D}) = \\underset{(s,a,r,s',d) \\sim {\\mathcal D}}{{\\mathrm E}}\\left[\n",
    "    \\left( Q_{\\phi_i}(s,a) - y(r,s',d) \\right)^2\n",
    "    \\right], \\;\\;\\;\\;\\; i=1,2$$\n",
    "    \n",
    "where the target is given by\n",
    "\n",
    "$$y(r, s', d) = r + \\gamma (1 - d) \\left( \\min_{i=1,2} Q_{\\phi_{\\text{targ},i}}(s', \\tilde{a}') - \\alpha \\log \\pi_{\\theta}(\\tilde{a}'|s') \\right), \\;\\;\\;\\;\\; \\tilde{a}' \\sim \\pi_{\\theta}(\\cdot|s')$$\n",
    "\n",
    "\n",
    "We now convert expectations to sample averages:\n",
    "\n",
    "$$L(\\phi_i, {\\mathcal D}) = \\frac{1}{|B|}\\sum_{(s,a,r,s',d) \\in B} \\left( Q_{\\phi_i}(s,a) - y(r,s',d) \\right)^2, \\;\\;\\;\\;\\; \\text{for } i=1,2$$\n",
    "\n",
    "The final Q Loss we will minimize is:\n",
    "\n",
    "$$Q_{Loss} = L(\\phi_1, {\\mathcal D}) + L(\\phi_2, {\\mathcal D})$$\n",
    "\n",
    "\n",
    "\n",
    "#### Policy Loss with Reparameterization Trick\n",
    "\n",
    "The policy should choose actions to maximize the expected future return and future entropy i.e. $V^\\pi\\left(s\\right)$:\n",
    "\n",
    "$$V^\\pi\\left(s\\right)= \\underset{a\\sim\\pi}{E}[Q^\\pi\\left(s,a\\right)+\\alpha H\\left(\\pi\\left(\\cdot\\middle| s\\right)\\right)]$$\n",
    "\n",
    "We rewrite this as:\n",
    "\n",
    "$$V^\\pi\\left(s\\right)= \\underset{a\\sim\\pi}{E}[Q^\\pi\\left(s,a\\right)\\ -\\ \\alpha\\ log\\ \\pi\\left(a\\middle| s\\right)]$$\n",
    "\n",
    "The Authors of the paper, use reparameterization along with squashed Gaussian policy:\n",
    "\n",
    "$$\\tilde{a_\\theta}\\left(s,\\xi\\right)=\\tanh{\\left(\\mu_\\theta\\left(s\\right)+\\sigma_\\theta\\left(s\\right)\\odot\\xi\\right)},\\;\\;\\;\\;\\;\\xi\\sim\\mathcal{N}\\left(0,I\\right)$$\n",
    "\n",
    "Combining last two equations, and also noting that our policy network is parameterized by θ, the policy network weights, we get:\n",
    "\n",
    "$$\\underset{a \\sim \\pi_{\\theta}}{E}[{Q^{\\pi_{\\theta}}(s,a) - \\alpha \\log \\pi_{\\theta}(a|s)}] = \\underset{\\xi \\sim \\mathcal{N}}{E}[{Q^{\\pi_{\\theta}}(s,\\tilde{a}_{\\theta}(s,\\xi)) - \\alpha \\log \\pi_{\\theta}(\\tilde{a}_{\\theta}(s,\\xi)|s)}]$$\n",
    "\n",
    "\n",
    "Next, we substitute the function approximator for Q taking min of the two Q-functions. \n",
    "\n",
    "$$ Q^{\\pi_{\\theta}}(s,\\tilde{a}_{\\theta}(s,\\xi)) = \\min_{i=1,2} Q_{\\phi_i}(s,\\tilde{a}_{\\theta}(s,\\xi))$$\n",
    "\n",
    "The Policy Objective is given by:\n",
    "\n",
    "$$\\max_{\\theta} \\underset{s \\sim \\mathcal{D} \\\\ \\xi \\sim \\mathcal{N}}{E}\\left[ {\\min_{i=1,2} Q_{\\phi_i}(s,\\tilde{a}_{\\theta}(s,\\xi)) - \\alpha \\log \\pi_{\\theta}(\\tilde{a}_{\\theta}(s,\\xi)|s)} \\right]$$\n",
    "\n",
    "Like before, we use minimizers in PyTorch/TensorFlow. Accordingly, we introduce a -ve sign to convert maximization to a Loss minimization:\n",
    "\n",
    "$$Policy_{Loss} = - \\underset{s \\sim \\mathcal{D} \\\\ \\xi \\sim \\mathcal{N}}{E}\\left[ {\\min_{i=1,2} Q_{\\phi_i}(s,\\tilde{a}_{\\theta}(s,\\xi)) - \\alpha \\log \\pi_{\\theta}(\\tilde{a}_{\\theta}(s,\\xi)|s)} \\right]$$\n",
    "\n",
    "Next we convert the expectation to estimate using samples to get:\n",
    "\n",
    "$$Policy_{Loss} = - \\frac{1}{|B|}\\sum_{s \\in B} \\left(\\min_{i=1,2} Q_{\\phi_i}(s, \\tilde{a}_{\\theta}(s)) - \\alpha \\log \\pi_{\\theta} \\left(\\left. \\tilde{a}_{\\theta}(s) \\right| s\\right) \\right)$$\n",
    "\n",
    "\n",
    "You can read more about Reparameterization and Stochastic Graphs in [Gradient Estimation Using\n",
    "Stochastic Computation Graphs](https://arxiv.org/pdf/1506.05254.pdf)\n",
    "\n",
    "\n",
    "\n",
    "***\n",
    "**Soft Actor Critic (SAC)**\n",
    "***\n",
    " \n",
    "1. Input initial policy parameters $\\theta$,  Q-function parameters $\\phi_1$ and $\\phi_2$, empty replay buffer D\n",
    "\n",
    "2. Set target parameters equal to online parameters $\\phi_{targ,1} \\leftarrow \\phi_1$ and $\\phi_{targ,2} \\leftarrow \\phi_2$\n",
    "\n",
    "3. **repeat**\n",
    "\n",
    "4. Observe state s and select action $a \\sim \\pi_{\\theta}(\\cdot|s)$\n",
    "\n",
    "5. Execute a in environment and observe next state s', reward r, and done signal d\n",
    "\n",
    "6. Store `(s,a,r,s',d)` in Replay Buffer D\n",
    "\n",
    "7. if `s'` is terminal state, reset the environment\n",
    "\n",
    "8. if it's time to update **then**:\n",
    "\n",
    "9. &emsp;&emsp;for j in range (as many updates as required):\n",
    "\n",
    "10. &emsp;&emsp;&emsp;&emsp;Sample a batch B={`(s,a,r,s',d)`} from replay Buffer D:\n",
    "\n",
    "11. &emsp;&emsp;&emsp;&emsp;Compute target for Q functions: \n",
    "\n",
    "$$y (r,s',d) = r + \\gamma (1-d) \\left(\\min_{i=1,2} Q_{\\phi_{\\text{targ}, i}} (s', \\tilde{a}') - \\alpha \\log \\pi_{\\theta}(\\tilde{a}'|s')\\right), \\;\\;\\; \\tilde{a}' \\sim \\pi_{\\theta}(\\cdot|s')$$\n",
    "\n",
    "12. &emsp;&emsp;&emsp;&emsp;Update Q function with one step gradient descent on $\\phi$:\n",
    "$$\\nabla_{\\phi_i} \\frac{1}{|B|}\\sum_{(s,a,r,s',d) \\in B} \\left( Q_{\\phi_i}(s,a) - y(r,s',d) \\right)^2, \\;\\;\\;\\;\\; \\text{for } i=1,2$$\n",
    "\n",
    "13. &emsp;&emsp;&emsp;&emsp;Update policy by one step of gradient ascent using:\n",
    "$$\\nabla_{\\theta} \\frac{1}{|B|}\\sum_{s \\in B} \\left(\\min_{i=1,2} Q_{\\phi_i}(s, \\tilde{a}_{\\theta}(s)) - \\alpha \\log \\pi_{\\theta} \\left(\\left. \\tilde{a}_{\\theta}(s) \\right| s\\right) \\right)$$\n",
    "                      \n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;where $\\tilde{a}_{\\theta}(s)$ is a sample from $\\pi_{\\theta}(\\cdot|s)$ which is differentiable wrt $\\theta$ via the reparameterization trick.\n",
    "\n",
    "14. &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;Update target networks using polyak averaging: \n",
    "$$\\phi_{targ,i} \\leftarrow \\rho\\phi_{targ,i} + (1-\\rho)\\phi_i, \\;\\;\\;\\;\\;  \\text{for } i=1,2$$ \n",
    "***\n",
    "\n",
    "\n",
    "#### Key Features of SAC\n",
    "\n",
    "1. SAC is an off-policy algorithm.\n",
    "2. SAC as implemented can only be used for environments with continuous action spaces. To apply SAC for discrete action space one needs to use Gumbel-Softmax distribution, You can find more about it online.\n",
    "3. SAC learns stochastic policies and is seen as extension of Actor Critic family. \n",
    "\n",
    "\n",
    "\n",
    "#### Our Implementation\n",
    "This notebook follows the code found in OpenAI's [Spinning Up Library](https://spinningup.openai.com/en/latest/algorithms/sac.html). In this notebook we have broken the code into separate code cells with required explanations. Also some of the implementations like ReplayBuffer have been borrowed from our past notebooks to provide continuity. Further some codes like building of networks have been simplified resulting in easier to understand but more verbose code. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running in Colab/Kaggle\n",
    "\n",
    "If you are running this on Colab, please uncomment below cells and run this to install required dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Uncomment and execute this cell to install all the the dependencies if running in Google Colab\n",
    "\n",
    "# !apt-get update && apt-get install swig cmake ffmpeg freeglut3-dev xvfb\n",
    "# !pip install box2d-py\n",
    "# !pip install \"stable-baselines3[extra]>=2.1\"\n",
    "# !pip install \"huggingface_sb3>=3.0\"\n",
    "\n",
    "# !pip install git+https://github.com/DLR-RM/rl-baselines3-zoo@update/hf\n",
    "# !git clone https://github.com/DLR-RM/rl-baselines3-zoo\n",
    "# %cd rl-baselines3-zoo/\n",
    "# !pip install -r requirements.txt\n",
    "# %cd ..\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.signal\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.optim import Adam\n",
    "from torch.distributions.normal import Normal\n",
    "\n",
    "from copy import deepcopy\n",
    "import gymnasium as gym\n",
    "\n",
    "from scipy.signal import convolve, gaussian\n",
    "from stable_baselines3.common.vec_env import VecVideoRecorder, DummyVecEnv\n",
    "\n",
    "from IPython.display import HTML, clear_output\n",
    "from base64 import b64encode\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Environment - Pendulum\n",
    "\n",
    "We can use the setup here to run on any environment which has state as a single vector and actions are continuous. We will build it on `Pendulum-v0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_env(env_name):\n",
    "    env = gym.make(env_name, render_mode=\"rgb_array\")\n",
    "    return env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State shape: (3,)\n",
      "Action shape: (1,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "env_name = 'Pendulum-v1'\n",
    "#env_name = 'MountainCarContinuous-v0'\n",
    "\n",
    "env = make_env(env_name)\n",
    "env.reset()\n",
    "plt.imshow(env.render())\n",
    "state_shape, action_shape = env.observation_space.shape, env.action_space.shape\n",
    "print('State shape: {}'.format(state_shape))\n",
    "print('Action shape: {}'.format(action_shape))\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Policy network (Actor)\n",
    "It is a simple 2 hidden layer network which takes state as input and produces the mean $\\mu$ and $\\log(\\sigma)$ of a normal distribution from which actions are sampled. Calculating $logprob$ requires understanding reparameterization of density distributions, a short explanation is given in Appendix C of the SAC paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOG_STD_MAX = 2\n",
    "LOG_STD_MIN = -5\n",
    "\n",
    "class SquashedGaussianMLPActor(nn.Module):\n",
    "    def __init__(self, state_dim, act_dim, act_limit):\n",
    "        super().__init__()\n",
    "        self.act_limit = act_limit\n",
    "        self.fc1 = nn.Linear(state_dim, 256)\n",
    "        self.fc2 = nn.Linear(256, 256)\n",
    "        self.mu_layer = nn.Linear(256, act_dim)\n",
    "        self.log_std_layer = nn.Linear(256, act_dim)\n",
    "    \n",
    "    def forward(self, s):\n",
    "        x = F.relu(self.fc1(s))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        mu = self.mu_layer(x)\n",
    "        log_std = self.log_std_layer(x)\n",
    "        log_std = torch.tanh(log_std)\n",
    "        log_std = LOG_STD_MIN + 0.5 * (LOG_STD_MAX - LOG_STD_MIN) * (log_std + 1)  # From SpinUp / Denis Yarats\n",
    "        return mu, log_std\n",
    "\n",
    "    def get_action(self, x):        \n",
    "        mean, log_std = self(x)\n",
    "        std = torch.exp(log_std)\n",
    "        # Pre-squash distribution and sample\n",
    "        pi_distribution = Normal(mean, std)\n",
    "        x_t = pi_distribution.rsample()\n",
    "        y_t = torch.tanh(x_t)\n",
    "        pi_action = self.act_limit * y_t\n",
    "        log_prob = pi_distribution.log_prob(x_t)\n",
    "        # Enforcing Action Bound\n",
    "        log_prob -= torch.log(self.act_limit * (1 - y_t.pow(2)) + 1e-6)\n",
    "        log_prob = log_prob.sum(-1, keepdim=True)\n",
    "        mean = torch.tanh(mean) * self.act_limit\n",
    "        return pi_action, log_prob, mean\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Q-network network (Critic)\n",
    "It is a simple 2 hidden layer network which takes state and action as input and produces Q-value as output. We will have two versions of Q-network as Critic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLPQFunction(nn.Module):\n",
    "    def __init__(self, state_dim, act_dim):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(state_dim+act_dim, 256)\n",
    "        self.fc2 = nn.Linear(256, 256)\n",
    "        self.Q = nn.Linear(256, 1)\n",
    "    \n",
    "    def forward(self, s, a):\n",
    "        x = torch.cat([s,a], dim=-1)\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        q = self.Q(x)\n",
    "        return q\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine Actor and Critic into a single model\n",
    "\n",
    "One policy Network and Two Q-networks as discussed above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLPActorCritic(nn.Module):\n",
    "    def __init__(self, observation_space, action_space):\n",
    "        super().__init__()\n",
    "        self.state_dim = observation_space.shape[0]\n",
    "        self.act_dim = action_space.shape[0]\n",
    "        self.act_limit = action_space.high[0]\n",
    "        \n",
    "        #build Q and policy functions\n",
    "        self.q1 = MLPQFunction(self.state_dim, self.act_dim)\n",
    "        self.q2 = MLPQFunction(self.state_dim, self.act_dim)\n",
    "        self.policy = SquashedGaussianMLPActor(self.state_dim, self.act_dim, self.act_limit)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experience replay\n",
    "\n",
    "We will use the replay buffer we saw in chapter 4 listings. Replay buffer is very important in DQN to break the correlation between samples. We use a behavior policy ($\\epsilon-greedy$) to sample from the environment and store the transitions (s,a,r,s',done) into a buffer. These samples are used multiple times in a learning making the process sample efficient. \n",
    "\n",
    "The interface to ReplayBuffer is:\n",
    "* `exp_replay.add(state, action, reward, next_state, done)` - saves (s,a,r,s',done) tuple into the buffer\n",
    "* `exp_replay.sample(batch_size)` - returns states, actions, rewards, next_states and done_flags for `batch_size` random samples.\n",
    "* `len(exp_replay)` - returns number of elements stored in replay buffer.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReplayBuffer:\n",
    "    def __init__(self, size=1e6):\n",
    "        self.size = size #max number of items in buffer\n",
    "        self.buffer =[] #array to hold buffer\n",
    "        self.next_id = 0\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.buffer)\n",
    "    \n",
    "    def add(self, state, action, reward, next_state, done):\n",
    "        item = (state, action, reward, next_state, done)\n",
    "        if len(self.buffer) < self.size:\n",
    "            self.buffer.append(item)\n",
    "        else:\n",
    "            self.buffer[self.next_id] = item\n",
    "        self.next_id = (self.next_id + 1) % self.size\n",
    "        \n",
    "    def sample(self, batch_size=32):\n",
    "        idxs = np.random.choice(len(self.buffer), batch_size)\n",
    "        samples = [self.buffer[i] for i in idxs]\n",
    "        states, actions, rewards, next_states, done_flags = list(zip(*samples))\n",
    "        return np.array(states), np.array(actions), np.array(rewards), np.array(next_states), np.array(done_flags)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q-loss\n",
    "\n",
    "Compute Q-loss as per equations above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_q_loss(agent, target_network, states, actions, rewards, next_states, done_flags,\n",
    "                    gamma, alpha, act_limit):\n",
    "    \n",
    "    # convert numpy array to torch tensors\n",
    "    states = torch.tensor(states, dtype=torch.float)\n",
    "    actions = torch.tensor(actions, dtype=torch.float)\n",
    "    rewards = torch.tensor(rewards, dtype=torch.float)\n",
    "    next_states = torch.tensor(next_states, dtype=torch.float)\n",
    "    done_flags = torch.tensor(done_flags.astype('float32'),dtype=torch.float)\n",
    "        \n",
    "    # Bellman backup for Q function\n",
    "    with torch.no_grad():\n",
    "        action_target, action_target_logp, _ = agent.policy.get_action(next_states)\n",
    "        # Target Q\n",
    "        q1_target = target_network.q1(next_states, action_target)\n",
    "        q2_target = target_network.q2(next_states, action_target)\n",
    "        q_target = torch.min(q1_target, q2_target).view(-1) - alpha*action_target_logp.view(-1)\n",
    "        target = rewards.view(-1) + gamma * (1 - done_flags.view(-1)) * q_target\n",
    "\n",
    "    # get q-values for all actions in current states\n",
    "    # use agent network\n",
    "    q1 = agent.q1(states, actions).view(-1)\n",
    "    q2 = agent.q2(states, actions).view(-1)\n",
    "\n",
    "    # MSE loss against Bellman backup\n",
    "    loss_q1 = F.mse_loss(q1, target)\n",
    "    loss_q2 = F.mse_loss(q2, target)\n",
    "    loss_q = loss_q1 + loss_q2\n",
    "    \n",
    "    return loss_q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Policy-Loss\n",
    "\n",
    "Compute Policy Loss as per equations above:\n",
    "    \n",
    "Please note the `-` sign. We need to do gradient ascent but PyTorch does gradient descent. We convert the ascent to descent using a -ve sign. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_policy_loss(agent, states, alpha):\n",
    "    \n",
    "    # convert numpy array to torch tensors\n",
    "    states = torch.tensor(states, dtype=torch.float)\n",
    "    \n",
    "    actions, actions_logp, _ = agent.policy.get_action(states)\n",
    "    \n",
    "    q1_values = agent.q1(states, actions)\n",
    "    q2_values = agent.q2(states, actions)\n",
    "    q_values = torch.min(q1_values, q2_values).view(-1)\n",
    "    \n",
    "    # Entropy regularised\n",
    "    loss_policy = - (q_values - alpha*actions_logp.view(-1)).mean()\n",
    "    return loss_policy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One step gradient Descent on both Policy(Actor) and Q-value(Critic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_step_update(agent, target_network, q_optimizer, policy_optimizer, a_optimizer,\n",
    "                    states, actions, rewards, next_states, done_flags, step, update_every,\n",
    "                    gamma, polyak, alpha, log_alpha, target_entropy, autotune, act_limit):\n",
    "    \n",
    "    #one step gradient for q-values\n",
    "    loss_q = compute_q_loss(agent, target_network, states, actions, rewards, next_states, done_flags,\n",
    "                    gamma, alpha, act_limit)\n",
    "    \n",
    "    q_optimizer.zero_grad()\n",
    "    loss_q.backward()\n",
    "    q_optimizer.step()\n",
    "\n",
    "    loss_policy_ret = None\n",
    "    if step % update_every == 0:\n",
    "        loss_policy_ret = []\n",
    "        for _ in range(update_every):\n",
    "            #one step gradient for policy network\n",
    "            loss_policy = compute_policy_loss(agent, states, alpha)\n",
    "            policy_optimizer.zero_grad()\n",
    "            loss_policy.backward()\n",
    "            policy_optimizer.step()\n",
    "            loss_policy_ret.append(loss_policy.item())\n",
    "            if autotune:\n",
    "                with torch.no_grad():\n",
    "                    states = torch.tensor(states, dtype=torch.float)\n",
    "                    _, log_pi, _ = agent.policy(states)\n",
    "                alpha_loss = (-log_alpha.exp() * (log_pi + target_entropy)).mean()\n",
    "                a_optimizer.zero_grad()\n",
    "                alpha_loss.backward()\n",
    "                a_optimizer.step()\n",
    "                alpha = log_alpha.exp().item()\n",
    "\n",
    "    # update target networks with polyak averaging\n",
    "    for params, params_target in zip(agent.parameters(), target_network.parameters()):\n",
    "        params_target.data.mul_(polyak)\n",
    "        params_target.data.add_((1-polyak)*params.data)\n",
    "\n",
    "    return loss_q.item(), loss_policy_ret, float(alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To Test performance of agent without any stochasticity i.e. perform deterministic actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_agent(env, agent, num_test_episodes, seed=42):\n",
    "    ep_rets, ep_lens = [], []\n",
    "    for j in range(num_test_episodes):\n",
    "        state, _ = env.reset(seed=seed)\n",
    "        done, ep_ret, ep_len = False, 0, 0\n",
    "        while not done:\n",
    "            state_inp = np.array(state, dtype = np.float32)[None,:]\n",
    "            state_inp = torch.tensor(state_inp, dtype=torch.float)\n",
    "            with torch.no_grad():\n",
    "                action, _, _ = agent.policy.get_action(state_inp)\n",
    "            action = action.detach().cpu().numpy()[0]\n",
    "            state, reward, terminated, truncated, _  = env.step(action)\n",
    "            ep_ret += reward\n",
    "            ep_len += 1\n",
    "            done = terminated or truncated\n",
    "        ep_rets.append(ep_ret)\n",
    "        ep_lens.append(ep_len)\n",
    "    return np.mean(ep_rets), np.mean(ep_lens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utility Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "def smoothen(values):\n",
    "    kernel = gaussian(100, std=100)\n",
    "    kernel = kernel / np.sum(kernel)\n",
    "    return convolve(values, kernel, 'valid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SAC Algorithm\n",
    "\n",
    "We pull all the pieces together to train the agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sac(env_fn, seed=0, \n",
    "         total_steps=15000, replay_size=20000, gamma=0.99, \n",
    "         polyak=0.995, policy_lr=3e-4, q_lr=1e-3, \n",
    "         alpha=0.2, autotune=False, batch_size=256, start_steps=5000, \n",
    "         update_every=2, num_test_episodes=3, eval_freq=500):\n",
    "    \n",
    "    torch.manual_seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    env, test_env = env_fn(), env_fn()\n",
    "    loss_q_history, loss_policy_history, return_history, length_history = [], [], [], []\n",
    "    state_dim = env.observation_space.shape\n",
    "    act_dim = env.action_space.shape[0]\n",
    "    act_limit = env.action_space.high[0]\n",
    "    \n",
    "    agent = MLPActorCritic(env.observation_space, env.action_space)\n",
    "    target_network = deepcopy(agent)\n",
    "    \n",
    "    # Experience buffer\n",
    "    replay_buffer = ReplayBuffer(replay_size)\n",
    "    \n",
    "    # List of parameters for both Q-networks \n",
    "    q_params = itertools.chain(agent.q1.parameters(), agent.q2.parameters())\n",
    "\n",
    "    if autotune:\n",
    "        target_entropy = -torch.prod(torch.Tensor(env.action_space.shape)).item()\n",
    "        log_alpha = torch.zeros(1, requires_grad=True)\n",
    "        alpha = log_alpha.exp().item()\n",
    "        a_optimizer = Adam([log_alpha], lr=q_lr)\n",
    "    else:\n",
    "        target_entropy = None\n",
    "        log_alpha = None\n",
    "        a_optimizer = None\n",
    "\n",
    "    #optimizers\n",
    "    q_optimizer = Adam(q_params, lr=q_lr)\n",
    "    policy_optimizer = Adam(agent.policy.parameters(), lr=policy_lr)\n",
    "    \n",
    "    state, _ = env.reset(seed=seed)\n",
    "    \n",
    "    for t in range(total_steps):\n",
    "        if t > start_steps:\n",
    "            state_inp = np.array(state, dtype = np.float32)[None,:]\n",
    "            state_inp = torch.tensor(state_inp, dtype=torch.float)\n",
    "            action, _, _ = agent.policy.get_action(state_inp)\n",
    "            action = action.detach().cpu().numpy()[0]\n",
    "        else:\n",
    "            action = env.action_space.sample()\n",
    "            \n",
    "        next_state, reward, terminated, truncated, _ = env.step(action)\n",
    "        # some environments do not terminate and therefore we get\n",
    "        # truncated signal from env\n",
    "        # we will use both terminated or truncated to reset the game\n",
    "        # so that agent can learn from all phases including starting phases\n",
    "        # however for Q target calculation will use only terminated as truncated\n",
    "        # is just a termination due to max time limit and not end of game\n",
    "        done = terminated\n",
    "        \n",
    "        # Store experience to replay buffer\n",
    "        replay_buffer.add(state, action, reward, next_state, done)\n",
    "        \n",
    "        # dont ever forget this step :)\n",
    "        state = next_state\n",
    "        \n",
    "        # End of trajectory handling\n",
    "        if terminated or truncated:\n",
    "            state, _ = env.reset(seed=seed+t)\n",
    "        \n",
    "        # Update handling\n",
    "        if t >= start_steps:\n",
    "                states, actions, rewards, next_states, done_flags = replay_buffer.sample(batch_size)\n",
    "                #print(states.shape, actions.shape, rewards.shape,next_states.shape,done_flags.shape)\n",
    "\n",
    "                loss_q, loss_policy, alpha = one_step_update(\n",
    "                        agent, target_network, q_optimizer, policy_optimizer, a_optimizer,\n",
    "                        states, actions, rewards, next_states, done_flags, t, update_every,\n",
    "                        gamma, polyak, alpha, log_alpha, target_entropy, autotune, act_limit\n",
    "                )\n",
    "                loss_q_history.append(loss_q)\n",
    "                if loss_policy:\n",
    "                    loss_policy_history+=loss_policy\n",
    "                    \n",
    "        # End of epoch handling\n",
    "        if t >= start_steps and t % eval_freq == 0:\n",
    "            avg_ret, avg_len = test_agent(test_env, agent, num_test_episodes)\n",
    "            return_history.append(avg_ret)\n",
    "            length_history.append(avg_len)\n",
    "\n",
    "            clear_output(True)\n",
    "            print(f'Steps:{t+1},loss:{loss_q_history[-1]:0.3f},{loss_policy[-1]:0.3f}, mean return:{avg_ret}, mean length:{avg_len}')\n",
    "            plt.figure(figsize=[16, 5])\n",
    "            plt.subplot(1, 3, 1)\n",
    "            plt.title(\"Mean return per episode\")\n",
    "            plt.plot(return_history)\n",
    "            plt.grid()\n",
    "\n",
    "            plt.subplot(1, 3, 2)\n",
    "            plt.title(\"Loss(Q)\")\n",
    "            plt.plot(smoothen(loss_q_history))\n",
    "            plt.grid()\n",
    "            \n",
    "            plt.subplot(1, 3, 3)\n",
    "            plt.title(\"Loss(Actor)\")\n",
    "            plt.plot(smoothen(loss_policy_history))\n",
    "            plt.grid()\n",
    "            \n",
    "            plt.show()\n",
    "    \n",
    "    return agent, loss_q_history, loss_policy_history, return_history, length_history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Steps:14501,loss:3.230,65.601, mean return:-118.45852397516292, mean length:200.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "env_name = 'Pendulum-v1'\n",
    "agent, _, _, _, _ = sac(lambda : make_env(env_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let us record a video of trained agent**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function to record videos\n",
    "def record_video(env_id, video_folder, video_length, agent):\n",
    "\n",
    "    vec_env = DummyVecEnv([lambda: gym.make(env_id, render_mode=\"rgb_array\")])\n",
    "    # Record the video starting at the first step\n",
    "    vec_env = VecVideoRecorder(vec_env, video_folder,\n",
    "                           record_video_trigger=lambda x: x == 0, video_length=video_length,\n",
    "                           name_prefix=f\"{type(agent).__name__}-{env_id}\")\n",
    "\n",
    "    obs = vec_env.reset()\n",
    "    for _ in range(video_length + 1):\n",
    "        with torch.no_grad():\n",
    "            action, _, _ = agent.policy.get_action(torch.tensor(obs))\n",
    "            action = action.detach().cpu().numpy()\n",
    "        obs, _, _, _ = vec_env.step(action)\n",
    "    # video filename\n",
    "    file_path = \"./\"+video_folder+vec_env.video_recorder.path.split(\"/\")[-1]\n",
    "    # Save the video\n",
    "    vec_env.close()\n",
    "    return file_path\n",
    "\n",
    "def play_video(file_path):\n",
    "    mp4 = open(file_path, 'rb').read()\n",
    "    data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
    "    return HTML(\"\"\"\n",
    "        <video width=400 controls>\n",
    "              <source src=\"%s\" type=\"video/mp4\">\n",
    "        </video>\n",
    "        \"\"\" % data_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving video to /home/nsanghi/sandbox/apress/drl-2ed/chapter9/logs/9_e/MLPActorCritic-Pendulum-v1-step-0-to-step-500.mp4\n",
      "Moviepy - Building video /home/nsanghi/sandbox/apress/drl-2ed/chapter9/logs/9_e/MLPActorCritic-Pendulum-v1-step-0-to-step-500.mp4.\n",
      "Moviepy - Writing video /home/nsanghi/sandbox/apress/drl-2ed/chapter9/logs/9_e/MLPActorCritic-Pendulum-v1-step-0-to-step-500.mp4\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                        \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moviepy - Done !\n",
      "Moviepy - video ready /home/nsanghi/sandbox/apress/drl-2ed/chapter9/logs/9_e/MLPActorCritic-Pendulum-v1-step-0-to-step-500.mp4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <video width=400 controls>\n",
       "              <source 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       "        </video>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_folder = \"logs/9_e/\"\n",
    "video_length = 500\n",
    "\n",
    "video_file = record_video(env_name, video_folder, video_length, agent)\n",
    "\n",
    "play_video(video_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train and evaluate performance on LunarLander Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train\n",
    "env_name = 'LunarLanderContinuous-v2'\n",
    "agent1, _, _, _, _ = sac(lambda : make_env(env_name))\n",
    "\n",
    "# Animate learned policy\n",
    "# Animate learned policy\n",
    "video_folder = \"logs/9_e/\"\n",
    "video_length = 1500\n",
    "video_file = record_video(env_name, video_folder, video_length, agent1)\n",
    "play_video(video_file)"
   ]
  }
 ],
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